School of CSE Seminar Series: Wei Jin

Speaker: Emory University Assistant Professor Wei Jin
Date and Time: January 19, 2:00-3:00 p.m.
Location: Coda 114
Host: B. Aditya Prakash

Title: Deep Learning on Graphs: A Data-Centric Exploration

Abstract: Many learning tasks in Artificial Intelligence require dealing with graph data, ranging from biology and chemistry to finance and education. Graph neural networks (GNNs), as deep learning models, have shown exceptional capabilities in learning from graph data. Despite their successes, GNNs often grapple with challenges stemming from data size and quality. This talk emphasizes a data-centric approach to enhance GNN performance. First, I will demonstrate methods to significantly reduce graph dataset sizes while retaining essential information for model training. Next, I will introduce a model-agnostic framework that enhances the quality of imperfect input graphs, thereby boosting prediction performance. These data-centric strategies not only enhance data efficiency and quality but also complement existing models. Finally, I will introduce recent advances in graph data valuation and graph generation. Join us to explore innovative approaches for overcoming data-related challenges in graph data mining.

Bio: Wei Jin is an Assistant Professor of Computer Science at Emory University. He obtained his Ph.D. from Michigan State University in 2023. His research focuses on graph machine learning and data-centric AI, with notable accomplishments such as AAAI New Faculty Highlights, KAUST Rising Star in AI, Snap Research Fellowship, Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. He has organized tutorials and workshops at top conferences, and published in top-tier venues such as ICLR, KDD, ICML, and NeurIPS. He has served as (senior) program committee members at these conferences and received the WSDM Outstanding Program Committee Member award.